Abstract
Magnetic resonance imaging (MRI) is a non-invasive technique used in cardiac diagnosis. Using it, specialists can measure the masses and volumes of the right ventricle (RV), left ventricular cavity (LVC), and myocardium (MYO). Segmenting these structures is an important step before this measurement. However, this process can be laborious and error-prone when done manually. This paper proposes a multi-stage method for cardiac segmentation in short-axis MRI based on fully convolutional networks (FCNs). This automatic method comprises three main stages: (1) the extraction of a region of interest (ROI); (2) MYO and LVC segmentation using a proposed FCN called EAIS-Net; and (3) the RV segmentation using another proposed FCN called IRAX-Net. The proposed method was tested with the ACDC and M&Ms datasets. The main evaluation metrics are end-diastolic (ED) and end-systolic (ES) Dice. For the ACDC dataset, the Dice results (ED and ES, respectively) are 0.960 and 0.904 for the LVC, 0.880 and 0.892 for the MYO, and 0.910 and 0.860 for the RV. For the M&Ms dataset, the ED and ES Dices are 0.861 and 0.805 for the LVC, 0.733 and 0.759 for the MYO, and 0.721 and 0.694 for the RV. These results confirm the feasibility of the proposed method.

Abstract
[No abstract available]

Abstract
The Eurasian grapevine (Vitis vinifera L.) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and inspection activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) and Machine Learning (ML) methods have emerged as a classification alternative to deal with the scarcity of ampelographs and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The classification pipeline of the 31 studies found in the literature was described, highlighting its pros and cons. Most of the studies used DL-based models trained with leaf images acquired in a controlled environment at a maximum distance of 1.2 metres to classify grape varieties. In addition, there is a large gap between practical applications and the datasets used: a great lack of varieties, limited data acquired in the field and a lack of tests on plants under adverse conditions. Potential directions for improving this area of research were also presented.

Abstract
The Eurasian grapevine (\textit{Vitis vinifera L.}) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role, as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and control activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) methods have emerged as a classification alternative to deal with the scarcity of ampelographers and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The steps of the standard DL-based classification pipeline were described for the 18 most relevant studies found in the literature, highlighting their pros and cons. Potential directions for improving this field of research were also presented.

Abstract
Pneumonia and COVID-19 are respiratory illnesses, the last caused by the severe acute respiratory syndrome virus, coronavirus 2 (SARS-CoV-2). Traditional detection processes can be slow, prone to errors, and laborious, leading to potential human mistakes and a limited ability to keep up with the speed of pathogen development. A web diagnosis application to aid the physician in the diagnosis process is presented, based on a modified deep neural network (AlexNet) to detect COVID-19 on X-rays and computed tomography (CT) scans as well as to detect pneumonia on X-rays. The system reached accuracy results well above 90% in seven well-known and documented datasets regarding the detection of COVID-19 and Pneumonia on X-rays and COVID-19 in CT scans.

Abstract
Fake news spreads rapidly, creating issues and making detection harder. The purpose of this study is to determine if fake news contains sentiment polarity (positive or negative), identify the polarity of sentiment present in their textual content and determine whether sentiment polarity is a reliable indication of fake news. For this, we use a deep learning model called BERT (Bidirectional Encoder Representations from Transformers), trained on a sentiment polarity dataset to classify the polarity of sentiments from a dataset of true and fake news. The findings show that sentiment polarity is not a reliable single feature for recognizing false news correctly and must be combined with other parameters to improve classification accuracy. © 2024 The Author(s). Published by Elsevier B.V.